Query scalar data across multiple products
POST
https://api.datadoghq.com/api/v2/query/scalarhttps://api.us3.datadoghq.com/api/v2/query/scalarhttps://api.us5.datadoghq.com/api/v2/query/scalarhttps://api.datadoghq.eu/api/v2/query/scalarhttps://api.ap1.datadoghq.com/api/v2/query/scalarhttps://api.ap2.datadoghq.com/api/v2/query/scalarhttps://api.uk1.datadoghq.com/api/v2/query/scalarhttps://api.ddog-gov.com/api/v2/query/scalarhttps://api.us2.ddog-gov.com/api/v2/query/scalarOverview
Query scalar values (as seen on Query Value, Table, and Toplist widgets). Multiple data sources are supported with the ability to process the data using formulas and functions.
OAuth apps require the timeseries_query authorization scope to access this endpoint.
Request
Body Data (required)
Field
Type
Description
data [required]object
A single scalar query to be executed.
attributes [required]object
The object describing a scalar formula request.
formulas[object]
List of formulas to be calculated and returned as responses.
formula [required]string
Formula string, referencing one or more queries with their name property.
limitobject
Message for specifying limits to the number of values returned by a query. This limit is only for scalar queries and has no effect on timeseries queries.
countint32
The number of results to which to limit.
orderenum
Direction of sort.
descAllowed enum values:
asc, descfrom [required]int64
Start date (inclusive) of the query in milliseconds since the Unix epoch.
queries [required][<oneOf>]
List of queries to be run and used as inputs to the formulas.
Object 1object
A query against Datadog custom metrics or Cloud Cost data sources.
aggregator [required]enum
The type of aggregation that can be performed on metrics-based queries.
avgAllowed enum values:
avg, min, max, sum, last, percentile, mean, l2norm, areacross_org_uuids[string]
Organization UUIDs to query when using cross-organization visibility. Limited to one organization UUID.
data_source [required]enum
A data source that is powered by the Metrics platform.
metricsAllowed enum values:
metrics, cloud_costnamestring
The variable name for use in formulas.
query [required]string
A classic metrics query string.
Object 2object
An individual scalar query for logs, RUM, traces, CI pipelines, security signals, and other event-based data sources. Use this query type for any data source powered by the Events Platform. See the data_source field for the full list of supported sources.
compute [required]object
The instructions for what to compute for this query.
aggregation [required]enum
The type of aggregation that can be performed on events-based queries.
countAllowed enum values:
count, cardinality, pc75, pc90, pc95, pc98, pc99, sum, min, maxShow 1 more
avgintervalint64
Interval for compute in milliseconds.
metricstring
The "measure" attribute on which to perform the computation.
cross_org_uuids[string]
Organization UUIDs to query when using cross-organization visibility. Limited to one organization UUID.
data_source [required]enum
A data source that is powered by the Events Platform.
logsAllowed enum values:
logs, spans, network, rum, security_signals, profiles, audit, events, ci_tests, ci_pipelinesShow 4 more
incident_analytics, product_analytics, on_call_events, doragroup_by[object]
The list of facets on which to split results.
facet [required]string
The facet by which to split groups.
limitint32
The maximum buckets to return for this group by. Note: at most 10000 buckets are allowed. If grouping by multiple facets, the product of limits must not exceed 10000.
10sortobject
The dimension by which to sort a query's results.
aggregation [required]enum
The type of aggregation that can be performed on events-based queries.
countAllowed enum values:
count, cardinality, pc75, pc90, pc95, pc98, pc99, sum, min, maxShow 1 more
avgmetricstring
The metric's calculated value which should be used to define the sort order of a query's results.
orderenum
Direction of sort.
descAllowed enum values:
asc, desctypeenum
The type of sort to use on the calculated value.
Allowed enum values:
alphabetical, measureindexes[string]
The indexes in which to search.
namestring
The variable name for use in formulas.
searchobject
Configuration of the search/filter for an events query.
querystring
The search/filter string for an events query.
<data_source=apm_resource_stats>object
A query for APM resource statistics such as latency, error rate, and hit count, grouped by resource name.
cross_org_uuids[string]
Organization UUIDs to query when using cross-organization visibility. Limited to one organization UUID.
data_source [required]enum
A data source for APM resource statistics queries.
apm_resource_statsAllowed enum values:
apm_resource_statsenv [required]string
The environment to query.
group_by[string]
Tag keys to group results by.
name [required]string
The variable name for use in formulas.
operation_namestring
The APM operation name.
primary_tag_namestring
Name of the second primary tag used within APM. Required when primary_tag_value is specified. See https://docs.datadoghq.com/tracing/guide/setting_primary_tags_to_scope/#add-a-second-primary-tag-in-datadog
primary_tag_valuestring
Value of the second primary tag by which to filter APM data. primary_tag_name must also be specified.
resource_namestring
The resource name to filter by.
service [required]string
The service name to filter by.
stat [required]enum
The APM resource statistic to query.
Allowed enum values:
error_rate, errors, hits, latency_avg, latency_max, latency_p50, latency_p75, latency_p90, latency_p95, latency_p99Show 2 more
latency_distribution, total_time<data_source=apm_metrics>object
A query for APM trace metrics such as hits, errors, and latency percentiles, aggregated across services.
cross_org_uuids[string]
Organization UUIDs to query when using cross-organization visibility. Limited to one organization UUID.
data_source [required]enum
A data source for APM metrics queries.
apm_metricsAllowed enum values:
apm_metricsgroup_by[string]
Optional fields to group the query results by.
name [required]string
The variable name for use in formulas.
operation_modestring
Optional operation mode to aggregate across operation names.
operation_namestring
Name of operation on service. If not provided, the primary operation name is used.
peer_tags[string]
Tags to query for a specific downstream entity (peer.service, peer.db_instance, peer.s3, peer.s3.bucket, etc.).
query_filterstring
Additional filters for the query using metrics query syntax (for example, env, primary_tag).
resource_hashstring
The resource hash for exact matching.
resource_namestring
The full name of a specific resource to filter by.
servicestring
The service name to filter by.
span_kindenum
Describes the relationship between the span, its parents, and its children in a trace.
Allowed enum values:
consumer, server, client, producer, internalstat [required]enum
The APM metric statistic to query.
Allowed enum values:
error_rate, errors, errors_per_second, hits, hits_per_second, apdex, latency_avg, latency_max, latency_p50, latency_p75Show 6 more
latency_p90, latency_p95, latency_p99, latency_p999, latency_distribution, total_time<data_source=apm_dependency_stats>object
A query for APM dependency statistics between services, such as call latency and error rates.
cross_org_uuids[string]
Organization UUIDs to query when using cross-organization visibility. Limited to one organization UUID.
data_source [required]enum
A data source for APM dependency statistics queries.
apm_dependency_statsAllowed enum values:
apm_dependency_statsenv [required]string
The environment to query.
is_upstreamboolean
Determines whether stats for upstream or downstream dependencies should be queried.
name [required]string
The variable name for use in formulas.
operation_name [required]string
The APM operation name.
primary_tag_namestring
The name of the second primary tag used within APM; required when primary_tag_value is specified. See https://docs.datadoghq.com/tracing/guide/setting_primary_tags_to_scope/#add-a-second-primary-tag-in-datadog.
primary_tag_valuestring
Filter APM data by the second primary tag. primary_tag_name must also be specified.
resource_name [required]string
The resource name to filter by.
service [required]string
The service name to filter by.
stat [required]enum
The APM dependency statistic to query.
Allowed enum values:
avg_duration, avg_root_duration, avg_spans_per_trace, error_rate, pct_exec_time, pct_of_traces, total_traces_count<data_source=slo>object
A query for SLO status, error budget, and burn rate metrics.
additional_query_filtersstring
Additional filters applied to the SLO query.
cross_org_uuids[string]
Organization UUIDs to query when using cross-organization visibility. Limited to one organization UUID.
data_source [required]enum
A data source for SLO queries.
sloAllowed enum values:
slogroup_modeenum
How SLO results are grouped in the response.
Allowed enum values:
overall, componentsmeasure [required]enum
The SLO measurement to retrieve.
Allowed enum values:
good_events, bad_events, slo_status, error_budget_remaining, error_budget_remaining_history, error_budget_burndown, burn_rate, slo_status_history, good_minutes, bad_minutesnamestring
The variable name for use in formulas.
slo_id [required]string
The unique identifier of the SLO to query.
slo_query_typeenum
The type of SLO definition being queried.
Allowed enum values:
metric, time_slice, monitor<data_source=process>object
Per-process metrics from Live Processes. Queries without aggregator fail. To scope to one process,
set tag_filters to its identifying_tags from GET /api/v2/processes.
aggregatorenum
The type of aggregation that can be performed on metrics-based queries.
avgAllowed enum values:
avg, min, max, sum, last, percentile, mean, l2norm, areacross_org_uuids[string]
Organization UUIDs to query when using cross-organization visibility. Limited to one organization UUID.
data_source [required]enum
A data source for process-level infrastructure metrics.
processAllowed enum values:
processis_normalized_cpuboolean
Whether CPU metrics should be normalized by core count.
limitint64
Maximum number of processes or groups to return. Large values can fail with an Unprocessable Content error. If so, retry with a smaller limit.
metric [required]string
The process metric to query, such as process.stat.cpu.total_pct, process.stat.memory.rss, or
process.stat.memory.vms.
name [required]string
The variable name for use in formulas.
sortenum
Direction of sort.
descAllowed enum values:
asc, desctag_filters[string]
Tags to narrow down processes. To scope to one process, use its identifying_tags from
GET /api/v2/processes, for example host:web-01 and pid:1234.
text_filterstring
A full-text search filter to match process names or commands.
<data_source=container>object
A query for container-level metrics such as CPU and memory usage.
aggregatorenum
The type of aggregation that can be performed on metrics-based queries.
avgAllowed enum values:
avg, min, max, sum, last, percentile, mean, l2norm, areacross_org_uuids[string]
Organization UUIDs to query when using cross-organization visibility. Limited to one organization UUID.
data_source [required]enum
A data source for container-level infrastructure metrics.
containerAllowed enum values:
containeris_normalized_cpuboolean
Whether CPU metrics should be normalized by core count.
limitint64
Maximum number of results to return.
metric [required]string
The container metric to query.
name [required]string
The variable name for use in formulas.
sortenum
Direction of sort.
descAllowed enum values:
asc, desctag_filters[string]
Tag filters to narrow down containers.
text_filterstring
A full-text search filter to match container names.
to [required]int64
End date (exclusive) of the query in milliseconds since the Unix epoch.
type [required]enum
The type of the resource. The value should always be scalar_request.
scalar_requestAllowed enum values:
scalar_request{
"data": {
"attributes": {
"formulas": [
{
"formula": "a+b",
"limit": {
"count": 10
}
}
],
"from": 1568899800000,
"queries": [
{
"aggregator": "avg",
"data_source": "metrics",
"query": "avg:system.cpu.user{*} by {env}"
}
],
"to": 1568923200000
},
"type": "scalar_request"
}
}Response
OK
A message containing one or more responses to scalar queries.
Field
Type
Description
dataobject
A message containing the response to a scalar query.
attributesobject
The object describing a scalar response.
columns[<oneOf>]
List of response columns, each corresponding to an individual formula or query in the request and with values in parallel arrays matching the series list.
<type=group>object
A column containing the tag keys and values in a group.
namestring
The name of the tag key or group.
typeenum
The type of column present for groups.
groupAllowed enum values:
groupvalues[array]
The array of tag values for each group found for the results of the formulas or queries.
<type=number>object
A column containing the numerical results for a formula or query.
metaobject
Metadata for the resulting numerical values.
unit[object]
Detailed information about the unit.
First element describes the "primary unit" (for example, bytes in bytes per second).
The second element describes the "per unit" (for example, second in bytes per second).
If the second element is not present, the API returns null.
familystring
Unit family, allows for conversion between units of the same family, for scaling.
namestring
Unit name
pluralstring
Plural form of the unit name.
scale_factordouble
Factor for scaling between units of the same family.
short_namestring
Abbreviation of the unit.
namestring
The name referencing the formula or query for this column.
typeenum
The type of column present for numbers.
numberAllowed enum values:
numbervalues[number]
The array of numerical values for one formula or query.
typeenum
The type of the resource. The value should always be scalar_response.
scalar_responseAllowed enum values:
scalar_responseerrorsstring
An error generated when processing a request.
{
"data": {
"attributes": {
"columns": [
{
"name": "env",
"type": "group",
"values": [
[
"staging"
]
]
}
]
},
"type": "scalar_response"
},
"errors": "string"
}Bad Request
API error response.
Field
Type
Description
errors [required][string]
A list of errors.
{
"errors": [
"Bad Request"
]
}Unauthorized
API error response.
Field
Type
Description
errors [required][string]
A list of errors.
{
"errors": [
"Bad Request"
]
}Forbidden
API error response.
Field
Type
Description
errors [required][string]
A list of errors.
{
"errors": [
"Bad Request"
]
}Too many requests
API error response.
Field
Type
Description
errors [required][string]
A list of errors.
{
"errors": [
"Bad Request"
]
}Code Example
# Set your Datadog site and credentials
export DD_SITE="datadoghq.com"
# Use a Personal Access Token or Service Access Token
export DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>"
curl -X POST "https://api.${DD_SITE}/api/v2/query/scalar" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${DD_BEARER_TOKEN}" \
-d @- << EOF
{
"data": {
"attributes": {
"formulas": [
{
"formula": "a+b",
"limit": {
"count": 10
}
}
],
"from": 1568899800000,
"queries": [
{
"aggregator": "avg",
"data_source": "metrics",
"query": "avg:system.cpu.user{*} by {env}"
}
],
"to": 1568923200000
},
"type": "scalar_request"
}
}
EOF# Set your Datadog site and credentials
export DD_SITE="us3.datadoghq.com"
# Use a Personal Access Token or Service Access Token
export DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>"
curl -X POST "https://api.${DD_SITE}/api/v2/query/scalar" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${DD_BEARER_TOKEN}" \
-d @- << EOF
{
"data": {
"attributes": {
"formulas": [
{
"formula": "a+b",
"limit": {
"count": 10
}
}
],
"from": 1568899800000,
"queries": [
{
"aggregator": "avg",
"data_source": "metrics",
"query": "avg:system.cpu.user{*} by {env}"
}
],
"to": 1568923200000
},
"type": "scalar_request"
}
}
EOF# Set your Datadog site and credentials
export DD_SITE="us5.datadoghq.com"
# Use a Personal Access Token or Service Access Token
export DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>"
curl -X POST "https://api.${DD_SITE}/api/v2/query/scalar" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${DD_BEARER_TOKEN}" \
-d @- << EOF
{
"data": {
"attributes": {
"formulas": [
{
"formula": "a+b",
"limit": {
"count": 10
}
}
],
"from": 1568899800000,
"queries": [
{
"aggregator": "avg",
"data_source": "metrics",
"query": "avg:system.cpu.user{*} by {env}"
}
],
"to": 1568923200000
},
"type": "scalar_request"
}
}
EOF# Set your Datadog site and credentials
export DD_SITE="datadoghq.eu"
# Use a Personal Access Token or Service Access Token
export DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>"
curl -X POST "https://api.${DD_SITE}/api/v2/query/scalar" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${DD_BEARER_TOKEN}" \
-d @- << EOF
{
"data": {
"attributes": {
"formulas": [
{
"formula": "a+b",
"limit": {
"count": 10
}
}
],
"from": 1568899800000,
"queries": [
{
"aggregator": "avg",
"data_source": "metrics",
"query": "avg:system.cpu.user{*} by {env}"
}
],
"to": 1568923200000
},
"type": "scalar_request"
}
}
EOF# Set your Datadog site and credentials
export DD_SITE="ap1.datadoghq.com"
# Use a Personal Access Token or Service Access Token
export DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>"
curl -X POST "https://api.${DD_SITE}/api/v2/query/scalar" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${DD_BEARER_TOKEN}" \
-d @- << EOF
{
"data": {
"attributes": {
"formulas": [
{
"formula": "a+b",
"limit": {
"count": 10
}
}
],
"from": 1568899800000,
"queries": [
{
"aggregator": "avg",
"data_source": "metrics",
"query": "avg:system.cpu.user{*} by {env}"
}
],
"to": 1568923200000
},
"type": "scalar_request"
}
}
EOF# Set your Datadog site and credentials
export DD_SITE="ap2.datadoghq.com"
# Use a Personal Access Token or Service Access Token
export DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>"
curl -X POST "https://api.${DD_SITE}/api/v2/query/scalar" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${DD_BEARER_TOKEN}" \
-d @- << EOF
{
"data": {
"attributes": {
"formulas": [
{
"formula": "a+b",
"limit": {
"count": 10
}
}
],
"from": 1568899800000,
"queries": [
{
"aggregator": "avg",
"data_source": "metrics",
"query": "avg:system.cpu.user{*} by {env}"
}
],
"to": 1568923200000
},
"type": "scalar_request"
}
}
EOF# Set your Datadog site and credentials
export DD_SITE="uk1.datadoghq.com"
# Use a Personal Access Token or Service Access Token
export DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>"
curl -X POST "https://api.${DD_SITE}/api/v2/query/scalar" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${DD_BEARER_TOKEN}" \
-d @- << EOF
{
"data": {
"attributes": {
"formulas": [
{
"formula": "a+b",
"limit": {
"count": 10
}
}
],
"from": 1568899800000,
"queries": [
{
"aggregator": "avg",
"data_source": "metrics",
"query": "avg:system.cpu.user{*} by {env}"
}
],
"to": 1568923200000
},
"type": "scalar_request"
}
}
EOF# Set your Datadog site and credentials
export DD_SITE="ddog-gov.com"
# Use a Personal Access Token or Service Access Token
export DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>"
curl -X POST "https://api.${DD_SITE}/api/v2/query/scalar" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${DD_BEARER_TOKEN}" \
-d @- << EOF
{
"data": {
"attributes": {
"formulas": [
{
"formula": "a+b",
"limit": {
"count": 10
}
}
],
"from": 1568899800000,
"queries": [
{
"aggregator": "avg",
"data_source": "metrics",
"query": "avg:system.cpu.user{*} by {env}"
}
],
"to": 1568923200000
},
"type": "scalar_request"
}
}
EOF# Set your Datadog site and credentials
export DD_SITE="us2.ddog-gov.com"
# Use a Personal Access Token or Service Access Token
export DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>"
curl -X POST "https://api.${DD_SITE}/api/v2/query/scalar" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer ${DD_BEARER_TOKEN}" \
-d @- << EOF
{
"data": {
"attributes": {
"formulas": [
{
"formula": "a+b",
"limit": {
"count": 10
}
}
],
"from": 1568899800000,
"queries": [
{
"aggregator": "avg",
"data_source": "metrics",
"query": "avg:system.cpu.user{*} by {env}"
}
],
"to": 1568923200000
},
"type": "scalar_request"
}
}
EOFScalar cross product query returns "OK" response
// Scalar cross product query returns "OK" response
package main
import (
"context"
"encoding/json"
"fmt"
"os"
"github.com/DataDog/datadog-api-client-go/v2/api/datadog"
"github.com/DataDog/datadog-api-client-go/v2/api/datadogV2"
)
func main() {
body := datadogV2.ScalarFormulaQueryRequest{
Data: datadogV2.ScalarFormulaRequest{
Attributes: datadogV2.ScalarFormulaRequestAttributes{
Formulas: []datadogV2.QueryFormula{
{
Formula: "a",
Limit: &datadogV2.FormulaLimit{
Count: datadog.PtrInt32(10),
Order: datadogV2.QUERYSORTORDER_DESC.Ptr(),
},
},
},
From: 1636625471000,
Queries: []datadogV2.ScalarQuery{
datadogV2.ScalarQuery{
MetricsScalarQuery: &datadogV2.MetricsScalarQuery{
Aggregator: datadogV2.METRICSAGGREGATOR_AVG,
DataSource: datadogV2.METRICSDATASOURCE_METRICS,
Query: "avg:system.cpu.user{*}",
Name: datadog.PtrString("a"),
}},
},
To: 1636629071000,
},
Type: datadogV2.SCALARFORMULAREQUESTTYPE_SCALAR_REQUEST,
},
}
ctx := datadog.NewDefaultContext(context.Background())
ctx = context.WithValue(ctx, datadog.ContextAccessToken, os.Getenv("DD_BEARER_TOKEN"))
configuration := datadog.NewConfiguration()
apiClient := datadog.NewAPIClient(configuration)
api := datadogV2.NewMetricsApi(apiClient)
resp, r, err := api.QueryScalarData(ctx, body)
if err != nil {
fmt.Fprintf(os.Stderr, "Error when calling `MetricsApi.QueryScalarData`: %v\n", err)
fmt.Fprintf(os.Stderr, "Full HTTP response: %v\n", r)
}
responseContent, _ := json.MarshalIndent(resp, "", " ")
fmt.Fprintf(os.Stdout, "Response from `MetricsApi.QueryScalarData`:\n%s\n", responseContent)
}
Scalar cross product query with RUM data source returns "OK" response
// Scalar cross product query with RUM data source returns "OK" response
package main
import (
"context"
"encoding/json"
"fmt"
"os"
"github.com/DataDog/datadog-api-client-go/v2/api/datadog"
"github.com/DataDog/datadog-api-client-go/v2/api/datadogV2"
)
func main() {
body := datadogV2.ScalarFormulaQueryRequest{
Data: datadogV2.ScalarFormulaRequest{
Attributes: datadogV2.ScalarFormulaRequestAttributes{
Formulas: []datadogV2.QueryFormula{
{
Formula: "a",
Limit: &datadogV2.FormulaLimit{
Count: datadog.PtrInt32(10),
Order: datadogV2.QUERYSORTORDER_DESC.Ptr(),
},
},
},
From: 1636625471000,
Queries: []datadogV2.ScalarQuery{
datadogV2.ScalarQuery{
EventsScalarQuery: &datadogV2.EventsScalarQuery{
DataSource: datadogV2.EVENTSDATASOURCE_RUM,
Name: datadog.PtrString("a"),
Compute: datadogV2.EventsCompute{
Aggregation: datadogV2.EVENTSAGGREGATION_COUNT,
},
Search: &datadogV2.EventsSearch{
Query: datadog.PtrString("*"),
},
Indexes: []string{
"*",
},
}},
},
To: 1636629071000,
},
Type: datadogV2.SCALARFORMULAREQUESTTYPE_SCALAR_REQUEST,
},
}
ctx := datadog.NewDefaultContext(context.Background())
ctx = context.WithValue(ctx, datadog.ContextAccessToken, os.Getenv("DD_BEARER_TOKEN"))
configuration := datadog.NewConfiguration()
apiClient := datadog.NewAPIClient(configuration)
api := datadogV2.NewMetricsApi(apiClient)
resp, r, err := api.QueryScalarData(ctx, body)
if err != nil {
fmt.Fprintf(os.Stderr, "Error when calling `MetricsApi.QueryScalarData`: %v\n", err)
fmt.Fprintf(os.Stderr, "Full HTTP response: %v\n", r)
}
responseContent, _ := json.MarshalIndent(resp, "", " ")
fmt.Fprintf(os.Stdout, "Response from `MetricsApi.QueryScalarData`:\n%s\n", responseContent)
}
Scalar cross product query with apm_dependency_stats data source returns "OK" response
// Scalar cross product query with apm_dependency_stats data source returns "OK" response
package main
import (
"context"
"encoding/json"
"fmt"
"os"
"github.com/DataDog/datadog-api-client-go/v2/api/datadog"
"github.com/DataDog/datadog-api-client-go/v2/api/datadogV2"
)
func main() {
body := datadogV2.ScalarFormulaQueryRequest{
Data: datadogV2.ScalarFormulaRequest{
Attributes: datadogV2.ScalarFormulaRequestAttributes{
Formulas: []datadogV2.QueryFormula{
{
Formula: "a",
Limit: &datadogV2.FormulaLimit{
Count: datadog.PtrInt32(10),
Order: datadogV2.QUERYSORTORDER_DESC.Ptr(),
},
},
},
From: 1636625471000,
Queries: []datadogV2.ScalarQuery{
datadogV2.ScalarQuery{
ApmDependencyStatsQuery: &datadogV2.ApmDependencyStatsQuery{
DataSource: datadogV2.APMDEPENDENCYSTATSDATASOURCE_APM_DEPENDENCY_STATS,
Name: "a",
Env: "ci",
Service: "cassandra",
Stat: datadogV2.APMDEPENDENCYSTATNAME_AVG_DURATION,
OperationName: "cassandra.query",
ResourceName: "DELETE FROM monitor_history.monitor_state_change_history WHERE org_id = ? AND monitor_id IN ? AND group = ?",
PrimaryTagName: datadog.PtrString("datacenter"),
PrimaryTagValue: datadog.PtrString("edge-eu1.prod.dog"),
}},
},
To: 1636629071000,
},
Type: datadogV2.SCALARFORMULAREQUESTTYPE_SCALAR_REQUEST,
},
}
ctx := datadog.NewDefaultContext(context.Background())
ctx = context.WithValue(ctx, datadog.ContextAccessToken, os.Getenv("DD_BEARER_TOKEN"))
configuration := datadog.NewConfiguration()
apiClient := datadog.NewAPIClient(configuration)
api := datadogV2.NewMetricsApi(apiClient)
resp, r, err := api.QueryScalarData(ctx, body)
if err != nil {
fmt.Fprintf(os.Stderr, "Error when calling `MetricsApi.QueryScalarData`: %v\n", err)
fmt.Fprintf(os.Stderr, "Full HTTP response: %v\n", r)
}
responseContent, _ := json.MarshalIndent(resp, "", " ")
fmt.Fprintf(os.Stdout, "Response from `MetricsApi.QueryScalarData`:\n%s\n", responseContent)
}
Instructions
First install the library and its dependencies and then save the example to main.go and run following commands:
DD_SITE="datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" go run "main.go"DD_SITE="us3.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" go run "main.go"DD_SITE="us5.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" go run "main.go"DD_SITE="datadoghq.eu" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" go run "main.go"DD_SITE="ap1.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" go run "main.go"DD_SITE="ap2.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" go run "main.go"DD_SITE="uk1.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" go run "main.go"DD_SITE="ddog-gov.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" go run "main.go"DD_SITE="us2.ddog-gov.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" go run "main.go"Scalar cross product query returns "OK" response
// Scalar cross product query returns "OK" response
import com.datadog.api.client.ApiClient;
import com.datadog.api.client.ApiException;
import com.datadog.api.client.v2.api.MetricsApi;
import com.datadog.api.client.v2.model.FormulaLimit;
import com.datadog.api.client.v2.model.MetricsAggregator;
import com.datadog.api.client.v2.model.MetricsDataSource;
import com.datadog.api.client.v2.model.MetricsScalarQuery;
import com.datadog.api.client.v2.model.QueryFormula;
import com.datadog.api.client.v2.model.QuerySortOrder;
import com.datadog.api.client.v2.model.ScalarFormulaQueryRequest;
import com.datadog.api.client.v2.model.ScalarFormulaQueryResponse;
import com.datadog.api.client.v2.model.ScalarFormulaRequest;
import com.datadog.api.client.v2.model.ScalarFormulaRequestAttributes;
import com.datadog.api.client.v2.model.ScalarFormulaRequestType;
import com.datadog.api.client.v2.model.ScalarQuery;
import java.util.Collections;
public class Example {
public static void main(String[] args) {
ApiClient defaultClient = ApiClient.getDefaultApiClient();
defaultClient.setAccessToken(System.getenv("DD_BEARER_TOKEN"));
MetricsApi apiInstance = new MetricsApi(defaultClient);
ScalarFormulaQueryRequest body =
new ScalarFormulaQueryRequest()
.data(
new ScalarFormulaRequest()
.attributes(
new ScalarFormulaRequestAttributes()
.formulas(
Collections.singletonList(
new QueryFormula()
.formula("a")
.limit(
new FormulaLimit()
.count(10)
.order(QuerySortOrder.DESC))))
.from(1636625471000L)
.queries(
Collections.singletonList(
new ScalarQuery(
new MetricsScalarQuery()
.aggregator(MetricsAggregator.AVG)
.dataSource(MetricsDataSource.METRICS)
.query("avg:system.cpu.user{*}")
.name("a"))))
.to(1636629071000L))
.type(ScalarFormulaRequestType.SCALAR_REQUEST));
try {
ScalarFormulaQueryResponse result = apiInstance.queryScalarData(body);
System.out.println(result);
} catch (ApiException e) {
System.err.println("Exception when calling MetricsApi#queryScalarData");
System.err.println("Status code: " + e.getCode());
System.err.println("Reason: " + e.getResponseBody());
System.err.println("Response headers: " + e.getResponseHeaders());
e.printStackTrace();
}
}
}
Scalar cross product query with RUM data source returns "OK" response
// Scalar cross product query with RUM data source returns "OK" response
import com.datadog.api.client.ApiClient;
import com.datadog.api.client.ApiException;
import com.datadog.api.client.v2.api.MetricsApi;
import com.datadog.api.client.v2.model.EventsAggregation;
import com.datadog.api.client.v2.model.EventsCompute;
import com.datadog.api.client.v2.model.EventsDataSource;
import com.datadog.api.client.v2.model.EventsScalarQuery;
import com.datadog.api.client.v2.model.EventsSearch;
import com.datadog.api.client.v2.model.FormulaLimit;
import com.datadog.api.client.v2.model.QueryFormula;
import com.datadog.api.client.v2.model.QuerySortOrder;
import com.datadog.api.client.v2.model.ScalarFormulaQueryRequest;
import com.datadog.api.client.v2.model.ScalarFormulaQueryResponse;
import com.datadog.api.client.v2.model.ScalarFormulaRequest;
import com.datadog.api.client.v2.model.ScalarFormulaRequestAttributes;
import com.datadog.api.client.v2.model.ScalarFormulaRequestType;
import com.datadog.api.client.v2.model.ScalarQuery;
import java.util.Collections;
public class Example {
public static void main(String[] args) {
ApiClient defaultClient = ApiClient.getDefaultApiClient();
defaultClient.setAccessToken(System.getenv("DD_BEARER_TOKEN"));
MetricsApi apiInstance = new MetricsApi(defaultClient);
ScalarFormulaQueryRequest body =
new ScalarFormulaQueryRequest()
.data(
new ScalarFormulaRequest()
.attributes(
new ScalarFormulaRequestAttributes()
.formulas(
Collections.singletonList(
new QueryFormula()
.formula("a")
.limit(
new FormulaLimit()
.count(10)
.order(QuerySortOrder.DESC))))
.from(1636625471000L)
.queries(
Collections.singletonList(
new ScalarQuery(
new EventsScalarQuery()
.dataSource(EventsDataSource.RUM)
.name("a")
.compute(
new EventsCompute()
.aggregation(EventsAggregation.COUNT))
.search(new EventsSearch().query("*"))
.indexes(Collections.singletonList("*")))))
.to(1636629071000L))
.type(ScalarFormulaRequestType.SCALAR_REQUEST));
try {
ScalarFormulaQueryResponse result = apiInstance.queryScalarData(body);
System.out.println(result);
} catch (ApiException e) {
System.err.println("Exception when calling MetricsApi#queryScalarData");
System.err.println("Status code: " + e.getCode());
System.err.println("Reason: " + e.getResponseBody());
System.err.println("Response headers: " + e.getResponseHeaders());
e.printStackTrace();
}
}
}
Scalar cross product query with apm_dependency_stats data source returns "OK" response
// Scalar cross product query with apm_dependency_stats data source returns "OK" response
import com.datadog.api.client.ApiClient;
import com.datadog.api.client.ApiException;
import com.datadog.api.client.v2.api.MetricsApi;
import com.datadog.api.client.v2.model.ApmDependencyStatName;
import com.datadog.api.client.v2.model.ApmDependencyStatsDataSource;
import com.datadog.api.client.v2.model.ApmDependencyStatsQuery;
import com.datadog.api.client.v2.model.FormulaLimit;
import com.datadog.api.client.v2.model.QueryFormula;
import com.datadog.api.client.v2.model.QuerySortOrder;
import com.datadog.api.client.v2.model.ScalarFormulaQueryRequest;
import com.datadog.api.client.v2.model.ScalarFormulaQueryResponse;
import com.datadog.api.client.v2.model.ScalarFormulaRequest;
import com.datadog.api.client.v2.model.ScalarFormulaRequestAttributes;
import com.datadog.api.client.v2.model.ScalarFormulaRequestType;
import com.datadog.api.client.v2.model.ScalarQuery;
import java.util.Collections;
public class Example {
public static void main(String[] args) {
ApiClient defaultClient = ApiClient.getDefaultApiClient();
defaultClient.setAccessToken(System.getenv("DD_BEARER_TOKEN"));
MetricsApi apiInstance = new MetricsApi(defaultClient);
ScalarFormulaQueryRequest body =
new ScalarFormulaQueryRequest()
.data(
new ScalarFormulaRequest()
.attributes(
new ScalarFormulaRequestAttributes()
.formulas(
Collections.singletonList(
new QueryFormula()
.formula("a")
.limit(
new FormulaLimit()
.count(10)
.order(QuerySortOrder.DESC))))
.from(1636625471000L)
.queries(
Collections.singletonList(
new ScalarQuery(
new ApmDependencyStatsQuery()
.dataSource(
ApmDependencyStatsDataSource.APM_DEPENDENCY_STATS)
.name("a")
.env("ci")
.service("cassandra")
.stat(ApmDependencyStatName.AVG_DURATION)
.operationName("cassandra.query")
.resourceName(
"DELETE FROM"
+ " monitor_history.monitor_state_change_history"
+ " WHERE org_id = ? AND monitor_id IN ? AND"
+ " group = ?")
.primaryTagName("datacenter")
.primaryTagValue("edge-eu1.prod.dog"))))
.to(1636629071000L))
.type(ScalarFormulaRequestType.SCALAR_REQUEST));
try {
ScalarFormulaQueryResponse result = apiInstance.queryScalarData(body);
System.out.println(result);
} catch (ApiException e) {
System.err.println("Exception when calling MetricsApi#queryScalarData");
System.err.println("Status code: " + e.getCode());
System.err.println("Reason: " + e.getResponseBody());
System.err.println("Response headers: " + e.getResponseHeaders());
e.printStackTrace();
}
}
}
Instructions
First install the library and its dependencies and then save the example to Example.java and run following commands:
DD_SITE="datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" java "Example.java"DD_SITE="us3.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" java "Example.java"DD_SITE="us5.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" java "Example.java"DD_SITE="datadoghq.eu" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" java "Example.java"DD_SITE="ap1.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" java "Example.java"DD_SITE="ap2.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" java "Example.java"DD_SITE="uk1.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" java "Example.java"DD_SITE="ddog-gov.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" java "Example.java"DD_SITE="us2.ddog-gov.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" java "Example.java"Scalar cross product query returns "OK" response
"""
Scalar cross product query returns "OK" response
"""
from os import environ
from datadog_api_client import ApiClient, Configuration
from datadog_api_client.v2.api.metrics_api import MetricsApi
from datadog_api_client.v2.model.formula_limit import FormulaLimit
from datadog_api_client.v2.model.metrics_aggregator import MetricsAggregator
from datadog_api_client.v2.model.metrics_data_source import MetricsDataSource
from datadog_api_client.v2.model.metrics_scalar_query import MetricsScalarQuery
from datadog_api_client.v2.model.query_formula import QueryFormula
from datadog_api_client.v2.model.query_sort_order import QuerySortOrder
from datadog_api_client.v2.model.scalar_formula_query_request import ScalarFormulaQueryRequest
from datadog_api_client.v2.model.scalar_formula_request import ScalarFormulaRequest
from datadog_api_client.v2.model.scalar_formula_request_attributes import ScalarFormulaRequestAttributes
from datadog_api_client.v2.model.scalar_formula_request_queries import ScalarFormulaRequestQueries
from datadog_api_client.v2.model.scalar_formula_request_type import ScalarFormulaRequestType
body = ScalarFormulaQueryRequest(
data=ScalarFormulaRequest(
attributes=ScalarFormulaRequestAttributes(
formulas=[
QueryFormula(
formula="a",
limit=FormulaLimit(
count=10,
order=QuerySortOrder.DESC,
),
),
],
_from=1636625471000,
queries=ScalarFormulaRequestQueries(
[
MetricsScalarQuery(
aggregator=MetricsAggregator.AVG,
data_source=MetricsDataSource.METRICS,
query="avg:system.cpu.user{*}",
name="a",
),
]
),
to=1636629071000,
),
type=ScalarFormulaRequestType.SCALAR_REQUEST,
),
)
configuration = Configuration()
configuration.access_token = environ["DD_BEARER_TOKEN"]
with ApiClient(configuration) as api_client:
api_instance = MetricsApi(api_client)
response = api_instance.query_scalar_data(body=body)
print(response)
Scalar cross product query with RUM data source returns "OK" response
"""
Scalar cross product query with RUM data source returns "OK" response
"""
from os import environ
from datadog_api_client import ApiClient, Configuration
from datadog_api_client.v2.api.metrics_api import MetricsApi
from datadog_api_client.v2.model.events_aggregation import EventsAggregation
from datadog_api_client.v2.model.events_compute import EventsCompute
from datadog_api_client.v2.model.events_data_source import EventsDataSource
from datadog_api_client.v2.model.events_scalar_query import EventsScalarQuery
from datadog_api_client.v2.model.events_search import EventsSearch
from datadog_api_client.v2.model.formula_limit import FormulaLimit
from datadog_api_client.v2.model.query_formula import QueryFormula
from datadog_api_client.v2.model.query_sort_order import QuerySortOrder
from datadog_api_client.v2.model.scalar_formula_query_request import ScalarFormulaQueryRequest
from datadog_api_client.v2.model.scalar_formula_request import ScalarFormulaRequest
from datadog_api_client.v2.model.scalar_formula_request_attributes import ScalarFormulaRequestAttributes
from datadog_api_client.v2.model.scalar_formula_request_queries import ScalarFormulaRequestQueries
from datadog_api_client.v2.model.scalar_formula_request_type import ScalarFormulaRequestType
body = ScalarFormulaQueryRequest(
data=ScalarFormulaRequest(
attributes=ScalarFormulaRequestAttributes(
formulas=[
QueryFormula(
formula="a",
limit=FormulaLimit(
count=10,
order=QuerySortOrder.DESC,
),
),
],
_from=1636625471000,
queries=ScalarFormulaRequestQueries(
[
EventsScalarQuery(
data_source=EventsDataSource.RUM,
name="a",
compute=EventsCompute(
aggregation=EventsAggregation.COUNT,
),
search=EventsSearch(
query="*",
),
indexes=[
"*",
],
),
]
),
to=1636629071000,
),
type=ScalarFormulaRequestType.SCALAR_REQUEST,
),
)
configuration = Configuration()
configuration.access_token = environ["DD_BEARER_TOKEN"]
with ApiClient(configuration) as api_client:
api_instance = MetricsApi(api_client)
response = api_instance.query_scalar_data(body=body)
print(response)
Scalar cross product query with apm_dependency_stats data source returns "OK" response
"""
Scalar cross product query with apm_dependency_stats data source returns "OK" response
"""
from os import environ
from datadog_api_client import ApiClient, Configuration
from datadog_api_client.v2.api.metrics_api import MetricsApi
from datadog_api_client.v2.model.apm_dependency_stat_name import ApmDependencyStatName
from datadog_api_client.v2.model.apm_dependency_stats_data_source import ApmDependencyStatsDataSource
from datadog_api_client.v2.model.apm_dependency_stats_query import ApmDependencyStatsQuery
from datadog_api_client.v2.model.formula_limit import FormulaLimit
from datadog_api_client.v2.model.query_formula import QueryFormula
from datadog_api_client.v2.model.query_sort_order import QuerySortOrder
from datadog_api_client.v2.model.scalar_formula_query_request import ScalarFormulaQueryRequest
from datadog_api_client.v2.model.scalar_formula_request import ScalarFormulaRequest
from datadog_api_client.v2.model.scalar_formula_request_attributes import ScalarFormulaRequestAttributes
from datadog_api_client.v2.model.scalar_formula_request_queries import ScalarFormulaRequestQueries
from datadog_api_client.v2.model.scalar_formula_request_type import ScalarFormulaRequestType
body = ScalarFormulaQueryRequest(
data=ScalarFormulaRequest(
attributes=ScalarFormulaRequestAttributes(
formulas=[
QueryFormula(
formula="a",
limit=FormulaLimit(
count=10,
order=QuerySortOrder.DESC,
),
),
],
_from=1636625471000,
queries=ScalarFormulaRequestQueries(
[
ApmDependencyStatsQuery(
data_source=ApmDependencyStatsDataSource.APM_DEPENDENCY_STATS,
name="a",
env="ci",
service="cassandra",
stat=ApmDependencyStatName.AVG_DURATION,
operation_name="cassandra.query",
resource_name="DELETE FROM monitor_history.monitor_state_change_history WHERE org_id = ? AND monitor_id IN ? AND group = ?",
primary_tag_name="datacenter",
primary_tag_value="edge-eu1.prod.dog",
),
]
),
to=1636629071000,
),
type=ScalarFormulaRequestType.SCALAR_REQUEST,
),
)
configuration = Configuration()
configuration.access_token = environ["DD_BEARER_TOKEN"]
with ApiClient(configuration) as api_client:
api_instance = MetricsApi(api_client)
response = api_instance.query_scalar_data(body=body)
print(response)
Instructions
First install the library and its dependencies and then save the example to example.py and run following commands:
DD_SITE="datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" python3 "example.py"DD_SITE="us3.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" python3 "example.py"DD_SITE="us5.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" python3 "example.py"DD_SITE="datadoghq.eu" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" python3 "example.py"DD_SITE="ap1.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" python3 "example.py"DD_SITE="ap2.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" python3 "example.py"DD_SITE="uk1.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" python3 "example.py"DD_SITE="ddog-gov.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" python3 "example.py"DD_SITE="us2.ddog-gov.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" python3 "example.py"Scalar cross product query returns "OK" response
# Scalar cross product query returns "OK" response
require "datadog_api_client"
DatadogAPIClient.configure do |config|
config.access_token = ENV["DD_BEARER_TOKEN"]
end
api_instance = DatadogAPIClient::V2::MetricsAPI.new
body = DatadogAPIClient::V2::ScalarFormulaQueryRequest.new({
data: DatadogAPIClient::V2::ScalarFormulaRequest.new({
attributes: DatadogAPIClient::V2::ScalarFormulaRequestAttributes.new({
formulas: [
DatadogAPIClient::V2::QueryFormula.new({
formula: "a",
limit: DatadogAPIClient::V2::FormulaLimit.new({
count: 10,
order: DatadogAPIClient::V2::QuerySortOrder::DESC,
}),
}),
],
from: 1636625471000,
queries: [
DatadogAPIClient::V2::MetricsScalarQuery.new({
aggregator: DatadogAPIClient::V2::MetricsAggregator::AVG,
data_source: DatadogAPIClient::V2::MetricsDataSource::METRICS,
query: "avg:system.cpu.user{*}",
name: "a",
}),
],
to: 1636629071000,
}),
type: DatadogAPIClient::V2::ScalarFormulaRequestType::SCALAR_REQUEST,
}),
})
p api_instance.query_scalar_data(body)
Scalar cross product query with RUM data source returns "OK" response
# Scalar cross product query with RUM data source returns "OK" response
require "datadog_api_client"
DatadogAPIClient.configure do |config|
config.access_token = ENV["DD_BEARER_TOKEN"]
end
api_instance = DatadogAPIClient::V2::MetricsAPI.new
body = DatadogAPIClient::V2::ScalarFormulaQueryRequest.new({
data: DatadogAPIClient::V2::ScalarFormulaRequest.new({
attributes: DatadogAPIClient::V2::ScalarFormulaRequestAttributes.new({
formulas: [
DatadogAPIClient::V2::QueryFormula.new({
formula: "a",
limit: DatadogAPIClient::V2::FormulaLimit.new({
count: 10,
order: DatadogAPIClient::V2::QuerySortOrder::DESC,
}),
}),
],
from: 1636625471000,
queries: [
DatadogAPIClient::V2::EventsScalarQuery.new({
data_source: DatadogAPIClient::V2::EventsDataSource::RUM,
name: "a",
compute: DatadogAPIClient::V2::EventsCompute.new({
aggregation: DatadogAPIClient::V2::EventsAggregation::COUNT,
}),
search: DatadogAPIClient::V2::EventsSearch.new({
query: "*",
}),
indexes: [
"*",
],
}),
],
to: 1636629071000,
}),
type: DatadogAPIClient::V2::ScalarFormulaRequestType::SCALAR_REQUEST,
}),
})
p api_instance.query_scalar_data(body)
Scalar cross product query with apm_dependency_stats data source returns "OK" response
# Scalar cross product query with apm_dependency_stats data source returns "OK" response
require "datadog_api_client"
DatadogAPIClient.configure do |config|
config.access_token = ENV["DD_BEARER_TOKEN"]
end
api_instance = DatadogAPIClient::V2::MetricsAPI.new
body = DatadogAPIClient::V2::ScalarFormulaQueryRequest.new({
data: DatadogAPIClient::V2::ScalarFormulaRequest.new({
attributes: DatadogAPIClient::V2::ScalarFormulaRequestAttributes.new({
formulas: [
DatadogAPIClient::V2::QueryFormula.new({
formula: "a",
limit: DatadogAPIClient::V2::FormulaLimit.new({
count: 10,
order: DatadogAPIClient::V2::QuerySortOrder::DESC,
}),
}),
],
from: 1636625471000,
queries: [
DatadogAPIClient::V2::ApmDependencyStatsQuery.new({
data_source: DatadogAPIClient::V2::ApmDependencyStatsDataSource::APM_DEPENDENCY_STATS,
name: "a",
env: "ci",
service: "cassandra",
stat: DatadogAPIClient::V2::ApmDependencyStatName::AVG_DURATION,
operation_name: "cassandra.query",
resource_name: "DELETE FROM monitor_history.monitor_state_change_history WHERE org_id = ? AND monitor_id IN ? AND group = ?",
primary_tag_name: "datacenter",
primary_tag_value: "edge-eu1.prod.dog",
}),
],
to: 1636629071000,
}),
type: DatadogAPIClient::V2::ScalarFormulaRequestType::SCALAR_REQUEST,
}),
})
p api_instance.query_scalar_data(body)
Instructions
First install the library and its dependencies and then save the example to example.rb and run following commands:
DD_SITE="datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" rb "example.rb"DD_SITE="us3.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" rb "example.rb"DD_SITE="us5.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" rb "example.rb"DD_SITE="datadoghq.eu" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" rb "example.rb"DD_SITE="ap1.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" rb "example.rb"DD_SITE="ap2.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" rb "example.rb"DD_SITE="uk1.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" rb "example.rb"DD_SITE="ddog-gov.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" rb "example.rb"DD_SITE="us2.ddog-gov.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" rb "example.rb"Scalar cross product query returns "OK" response
// Scalar cross product query returns "OK" response
use datadog_api_client::datadog;
use datadog_api_client::datadogV2::api_metrics::MetricsAPI;
use datadog_api_client::datadogV2::model::FormulaLimit;
use datadog_api_client::datadogV2::model::MetricsAggregator;
use datadog_api_client::datadogV2::model::MetricsDataSource;
use datadog_api_client::datadogV2::model::MetricsScalarQuery;
use datadog_api_client::datadogV2::model::QueryFormula;
use datadog_api_client::datadogV2::model::QuerySortOrder;
use datadog_api_client::datadogV2::model::ScalarFormulaQueryRequest;
use datadog_api_client::datadogV2::model::ScalarFormulaRequest;
use datadog_api_client::datadogV2::model::ScalarFormulaRequestAttributes;
use datadog_api_client::datadogV2::model::ScalarFormulaRequestType;
use datadog_api_client::datadogV2::model::ScalarQuery;
#[tokio::main]
async fn main() {
let body = ScalarFormulaQueryRequest::new(ScalarFormulaRequest::new(
ScalarFormulaRequestAttributes::new(
1636625471000,
vec![ScalarQuery::MetricsScalarQuery(Box::new(
MetricsScalarQuery::new(
MetricsAggregator::AVG,
MetricsDataSource::METRICS,
"avg:system.cpu.user{*}".to_string(),
)
.name("a".to_string()),
))],
1636629071000,
)
.formulas(vec![QueryFormula::new("a".to_string())
.limit(FormulaLimit::new().count(10).order(QuerySortOrder::DESC))]),
ScalarFormulaRequestType::SCALAR_REQUEST,
));
let configuration = datadog::Configuration::new();
let api = MetricsAPI::with_config(configuration);
let resp = api.query_scalar_data(body).await;
if let Ok(value) = resp {
println!("{:#?}", value);
} else {
println!("{:#?}", resp.unwrap_err());
}
}
Scalar cross product query with RUM data source returns "OK" response
// Scalar cross product query with RUM data source returns "OK" response
use datadog_api_client::datadog;
use datadog_api_client::datadogV2::api_metrics::MetricsAPI;
use datadog_api_client::datadogV2::model::EventsAggregation;
use datadog_api_client::datadogV2::model::EventsCompute;
use datadog_api_client::datadogV2::model::EventsDataSource;
use datadog_api_client::datadogV2::model::EventsScalarQuery;
use datadog_api_client::datadogV2::model::EventsSearch;
use datadog_api_client::datadogV2::model::FormulaLimit;
use datadog_api_client::datadogV2::model::QueryFormula;
use datadog_api_client::datadogV2::model::QuerySortOrder;
use datadog_api_client::datadogV2::model::ScalarFormulaQueryRequest;
use datadog_api_client::datadogV2::model::ScalarFormulaRequest;
use datadog_api_client::datadogV2::model::ScalarFormulaRequestAttributes;
use datadog_api_client::datadogV2::model::ScalarFormulaRequestType;
use datadog_api_client::datadogV2::model::ScalarQuery;
#[tokio::main]
async fn main() {
let body = ScalarFormulaQueryRequest::new(ScalarFormulaRequest::new(
ScalarFormulaRequestAttributes::new(
1636625471000,
vec![ScalarQuery::EventsScalarQuery(Box::new(
EventsScalarQuery::new(
EventsCompute::new(EventsAggregation::COUNT),
EventsDataSource::RUM,
)
.indexes(vec!["*".to_string()])
.name("a".to_string())
.search(EventsSearch::new().query("*".to_string())),
))],
1636629071000,
)
.formulas(vec![QueryFormula::new("a".to_string())
.limit(FormulaLimit::new().count(10).order(QuerySortOrder::DESC))]),
ScalarFormulaRequestType::SCALAR_REQUEST,
));
let configuration = datadog::Configuration::new();
let api = MetricsAPI::with_config(configuration);
let resp = api.query_scalar_data(body).await;
if let Ok(value) = resp {
println!("{:#?}", value);
} else {
println!("{:#?}", resp.unwrap_err());
}
}
Scalar cross product query with apm_dependency_stats data source returns "OK" response
// Scalar cross product query with apm_dependency_stats data source returns "OK"
// response
use datadog_api_client::datadog;
use datadog_api_client::datadogV2::api_metrics::MetricsAPI;
use datadog_api_client::datadogV2::model::ApmDependencyStatName;
use datadog_api_client::datadogV2::model::ApmDependencyStatsDataSource;
use datadog_api_client::datadogV2::model::ApmDependencyStatsQuery;
use datadog_api_client::datadogV2::model::FormulaLimit;
use datadog_api_client::datadogV2::model::QueryFormula;
use datadog_api_client::datadogV2::model::QuerySortOrder;
use datadog_api_client::datadogV2::model::ScalarFormulaQueryRequest;
use datadog_api_client::datadogV2::model::ScalarFormulaRequest;
use datadog_api_client::datadogV2::model::ScalarFormulaRequestAttributes;
use datadog_api_client::datadogV2::model::ScalarFormulaRequestType;
use datadog_api_client::datadogV2::model::ScalarQuery;
#[tokio::main]
async fn main() {
let body =
ScalarFormulaQueryRequest::new(
ScalarFormulaRequest::new(
ScalarFormulaRequestAttributes::new(
1636625471000,
vec![
ScalarQuery::ApmDependencyStatsQuery(
Box::new(
ApmDependencyStatsQuery::new(
ApmDependencyStatsDataSource::APM_DEPENDENCY_STATS,
"ci".to_string(),
"a".to_string(),
"cassandra.query".to_string(),
"DELETE FROM monitor_history.monitor_state_change_history WHERE org_id = ? AND monitor_id IN ? AND group = ?".to_string(),
"cassandra".to_string(),
ApmDependencyStatName::AVG_DURATION,
)
.primary_tag_name("datacenter".to_string())
.primary_tag_value("edge-eu1.prod.dog".to_string()),
),
)
],
1636629071000,
).formulas(
vec![
QueryFormula::new(
"a".to_string(),
).limit(FormulaLimit::new().count(10).order(QuerySortOrder::DESC))
],
),
ScalarFormulaRequestType::SCALAR_REQUEST,
),
);
let configuration = datadog::Configuration::new();
let api = MetricsAPI::with_config(configuration);
let resp = api.query_scalar_data(body).await;
if let Ok(value) = resp {
println!("{:#?}", value);
} else {
println!("{:#?}", resp.unwrap_err());
}
}
Instructions
First install the library and its dependencies and then save the example to src/main.rs and run following commands:
DD_SITE="datadoghq.com" DD_API_KEY="<DD_API_KEY>" DD_APP_KEY="<DD_APP_KEY>" cargo runDD_SITE="us3.datadoghq.com" DD_API_KEY="<DD_API_KEY>" DD_APP_KEY="<DD_APP_KEY>" cargo runDD_SITE="us5.datadoghq.com" DD_API_KEY="<DD_API_KEY>" DD_APP_KEY="<DD_APP_KEY>" cargo runDD_SITE="datadoghq.eu" DD_API_KEY="<DD_API_KEY>" DD_APP_KEY="<DD_APP_KEY>" cargo runDD_SITE="ap1.datadoghq.com" DD_API_KEY="<DD_API_KEY>" DD_APP_KEY="<DD_APP_KEY>" cargo runDD_SITE="ap2.datadoghq.com" DD_API_KEY="<DD_API_KEY>" DD_APP_KEY="<DD_APP_KEY>" cargo runDD_SITE="uk1.datadoghq.com" DD_API_KEY="<DD_API_KEY>" DD_APP_KEY="<DD_APP_KEY>" cargo runDD_SITE="ddog-gov.com" DD_API_KEY="<DD_API_KEY>" DD_APP_KEY="<DD_APP_KEY>" cargo runDD_SITE="us2.ddog-gov.com" DD_API_KEY="<DD_API_KEY>" DD_APP_KEY="<DD_APP_KEY>" cargo runScalar cross product query returns "OK" response
/**
* Scalar cross product query returns "OK" response
*/
import { client, v2 } from "@datadog/datadog-api-client";
const configuration = client.createConfiguration({
authMethods: {
AuthZ: {
accessToken: process.env.DD_BEARER_TOKEN as string,
},
},
});
const apiInstance = new v2.MetricsApi(configuration);
const params: v2.MetricsApiQueryScalarDataRequest = {
body: {
data: {
attributes: {
formulas: [
{
formula: "a",
limit: {
count: 10,
order: "desc",
},
},
],
from: 1636625471000,
queries: [
{
aggregator: "avg",
dataSource: "metrics",
query: "avg:system.cpu.user{*}",
name: "a",
},
],
to: 1636629071000,
},
type: "scalar_request",
},
},
};
apiInstance
.queryScalarData(params)
.then((data: v2.ScalarFormulaQueryResponse) => {
console.log(
"API called successfully. Returned data: " + JSON.stringify(data)
);
})
.catch((error: any) => console.error(error));
Scalar cross product query with RUM data source returns "OK" response
/**
* Scalar cross product query with RUM data source returns "OK" response
*/
import { client, v2 } from "@datadog/datadog-api-client";
const configuration = client.createConfiguration({
authMethods: {
AuthZ: {
accessToken: process.env.DD_BEARER_TOKEN as string,
},
},
});
const apiInstance = new v2.MetricsApi(configuration);
const params: v2.MetricsApiQueryScalarDataRequest = {
body: {
data: {
attributes: {
formulas: [
{
formula: "a",
limit: {
count: 10,
order: "desc",
},
},
],
from: 1636625471000,
queries: [
{
dataSource: "rum",
name: "a",
compute: {
aggregation: "count",
},
search: {
query: "*",
},
indexes: ["*"],
},
],
to: 1636629071000,
},
type: "scalar_request",
},
},
};
apiInstance
.queryScalarData(params)
.then((data: v2.ScalarFormulaQueryResponse) => {
console.log(
"API called successfully. Returned data: " + JSON.stringify(data)
);
})
.catch((error: any) => console.error(error));
Scalar cross product query with apm_dependency_stats data source returns "OK" response
/**
* Scalar cross product query with apm_dependency_stats data source returns "OK" response
*/
import { client, v2 } from "@datadog/datadog-api-client";
const configuration = client.createConfiguration({
authMethods: {
AuthZ: {
accessToken: process.env.DD_BEARER_TOKEN as string,
},
},
});
const apiInstance = new v2.MetricsApi(configuration);
const params: v2.MetricsApiQueryScalarDataRequest = {
body: {
data: {
attributes: {
formulas: [
{
formula: "a",
limit: {
count: 10,
order: "desc",
},
},
],
from: 1636625471000,
queries: [
{
dataSource: "apm_dependency_stats",
name: "a",
env: "ci",
service: "cassandra",
stat: "avg_duration",
operationName: "cassandra.query",
resourceName:
"DELETE FROM monitor_history.monitor_state_change_history WHERE org_id = ? AND monitor_id IN ? AND group = ?",
primaryTagName: "datacenter",
primaryTagValue: "edge-eu1.prod.dog",
},
],
to: 1636629071000,
},
type: "scalar_request",
},
},
};
apiInstance
.queryScalarData(params)
.then((data: v2.ScalarFormulaQueryResponse) => {
console.log(
"API called successfully. Returned data: " + JSON.stringify(data)
);
})
.catch((error: any) => console.error(error));
Instructions
First install the library and its dependencies and then save the example to example.ts and run following commands:
DD_SITE="datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" tsc "example.ts"DD_SITE="us3.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" tsc "example.ts"DD_SITE="us5.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" tsc "example.ts"DD_SITE="datadoghq.eu" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" tsc "example.ts"DD_SITE="ap1.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" tsc "example.ts"DD_SITE="ap2.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" tsc "example.ts"DD_SITE="uk1.datadoghq.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" tsc "example.ts"DD_SITE="ddog-gov.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" tsc "example.ts"DD_SITE="us2.ddog-gov.com" DD_BEARER_TOKEN="<PERSONAL_ACCESS_TOKEN OR SERVICE_ACCESS_TOKEN>" tsc "example.ts"